Growing degree-hours and degree-days in two management zones for each phenological stage of wheat (Triticum aestivum L.).

Monitoring the climatic conditions of crops is essential for smart agriculture development and adaptation of agricultural systems in the era of global change. Thereby, it is possibly better to understand the stages of development of the crop, thus adopting management practices more efficiently and planning the harvest with greater accuracy. This study was developed to analyze the growing degree-hours and degree-days in two management zones (MZs) for each phenological stage of wheat (Triticum aestivum L.) and the application of low-cost agroclimatological stations to monitor the climatic conditions of the field production. The study was developed in a Ferralsol in Céu-Azul/Brazil. Ten low-cost agrometeorological stations were installed in two MZs delineated based on elevation data using the web platform AgDataBox. Data on solar radiation, atmospheric pressure, wind speed, precipitation, relative humidity, air, and soil temperature were evaluated over two wheat crop seasons. Our results showed different climatic conditions, especially humidity and temperature, between MZs and crop seasons, which could probably cause yield variability. By the low-cost agroclimatological stations, it is possible to collect data on the thermal accumulation by the culture in growing degree-hours, which is a more accurate parameter than the growing degree-days (commonly used in similar studies). With the growing degree-hours data, it was possible to follow the development of the phenological stages of wheat. In conclusion, the results obtained suggest the importance of evaluating agroclimatological parameters in monitoring wheat crops. However, more studies are needed in regions with greater slopes, which may have microclimates that intensely influence the crop.

Unveiling the mechanisms of carboxylic acid esterification on acid zeolites for biomass-to-energy: A review of the catalytic process through experimental and computational studies.

In recent years, there has been significant interest from industrial and academic areas in the esterification of carboxylic acids catalyzed by acidic zeolites, as it represents a sustainable and economically viable approach to producing a wide range of high-value-added products. However, there is a lack of comprehensive reviews that address the intricate reaction mechanisms occurring at the catalyst interface at both the experimental and atomistic levels. Therefore, in this review, we provide an overview of the esterification reaction on acidic zeolites based on experimental and theoretical studies. The combination of infrared spectroscopy with atomistic calculations and experimental strategies using modulation excitation spectroscopy techniques combined with phase-sensitive detection is presented as an approach to detecting short-lived intermediates at the interface of zeolitic frameworks under realistic reaction conditions. To achieve this goal, this review has been divided into four sections: The first is a brief introduction highlighting the distinctive features of this review. The second addresses questions about the topology and activity of different zeolitic systems, since these properties are closely correlated in the esterification process. The third section deals with the mechanisms proposed in the literature. The fourth section presents advances in IR techniques and theoretical calculations that can be applied to gain new insights into reaction mechanisms. Finally, this review concludes with a subtle approach, highlighting the main aspects and perspectives of combining experimental and theoretical techniques to elucidate different reaction mechanisms in zeolitic systems.

AgDataBox-IoT - application development for agrometeorological stations in smart.

Currently, Brazil is one of the world's largest grain producers and exporters. Agriculture has already entered its 4.0 version (2017), also known as digital agriculture, when the industry has entered the 4.0 era (2011). This new paradigm uses Internet of Things (IoT) techniques, sensors installed in the field, network of interconnected sensors in the plot, drones for crop monitoring, multispectral cameras, storage and processing of data in Cloud Computing, and Big Data techniques to process the large volumes of generated data. One of the practical options for implementing precision agriculture is the segmentation of the plot into management zones, aiming at maximizing profits according to the productive potential of each zone, being economically viable even for small producers. Considering that climate factors directly influence yield, this study describes the development of a sensor network for climate monitoring of management zones (microclimates), allowing the identification of climate factors that influence yield at each of its stages.•Application of the internet of things to assist in decision making in the agricultural production system.•AgDataBox (ADB-IoT) web platform has an Application Programming Interface (API).•An agrometeorological station capable of monitoring all meteorological parameters was developed (Kate 3.0).

Productivity of soybean in management zones with application of different sowing densities / Produtividade da soja em zonas de manejo com a aplicação de diferentes densidades de semeadura

ABSTRACT: The present study aimed to assess the efficiency of sowing at variable rates for soybean cultivation in two management zones (MZs) which were defined based on stable attributes and correlated with productivity using the Fuzzy C-means clustering algorithm and the kriging interpolation.Seeding was carried out in the 2015/2016 and 2017/2018 crops with a variation of 20% of seeds and crop row spacing of 0.70m. In each MZ, 8 plots with higher and lower seed density were established. Productivity was measured using a harvest monitor connected to a harvester. Data were filtered and submitted to descriptive analysis. Productivity maps were generated using the inverse square distance interpolation for each seeding density. In the MZ with the highest productive potential (MZ 1), the productivity was 3.39 and 3.18t ha-1, and in the MZ with the lowest productive potential (MZ 2) the productivity was 3.30 and 3.11t ha-1 for the years 2016 and 2018, respectively. Interpolation estimated higher productivity with the application of 15 plants m-1. Based on the economic analysis, it is suggested in this study the application of 214,000 plants ha-1 in both MZs.

RESUMO: O trabalho avaliou a eficiência da semeadura à taxa variada para cultura da soja em duas zonas de manejo (ZMs), as quais foram definidas com base em atributos estáveis e correlacionados com a produtividade, por meio do algoritmo de agrupamento Fuzzy C-means e o interpolador krigagem. A semeadura foi realizada nas safras 2015/2016 e 2017/2018, com variação de 20% de sementes e espaçamento entre linhas de 0,70m. Em cada ZM foram estabelecidas 8 parcelas em que variou-se maior e menor densidade de sementes. A produtividade foi medida com monitor de colheita acoplado em uma colhedora. Os dados foram filtrados e submetidos à análise descritiva, os mapas de produtividade foram gerados utilizando-se o interpolador inverso do quadrado da distância para cada densidade de semeadura. Na ZM com maior potencial produtivo (ZM 1) a produtividade foi de 3,39 e 3,18t ha-1, na ZM de menor potencial produtivo (ZM 2) foi de 3,30 e 3,11t ha-1, para os anos de 2016 e 2018, respectivamente. O interpolador estimou maior produtividade com a aplicação de 15 plantas m-1; pela análise econômica, sugere-se, dentro do estudado, a aplicação de 214.000 plantas ha-1 nas duas ZMs.